美国加州大学圣迭戈分校任兵团队开发出一种快速、可扩展、多功能的单细胞组学数据分析工具。相关论文于2024年1月8日在线发表在《自然—方法学》杂志上。

研究人员介绍了一种非线性降维算法，该算法体现在Python软件包SnapATAC2中，它不仅能更精确地捕捉单细胞多组学数据的异质性，还能确保高效的运行时间和内存使用，并与细胞数量成线性比例。这个算法在不同的单细胞组学数据集（包括利用测序技术对转座酶可接触染色质的单细胞检测、单细胞RNA测序、单细胞Hi-C和单细胞多组学数据集）中都表现出了卓越的性能、可扩展性和多功能性，从而突出了它在推进单细胞分析方面的实用性。

据悉，单细胞组学技术彻底改变了对复杂组织中基因调控的研究。分析这些数据集的一个主要计算挑战是将大规模高维数据投影到低维空间，同时保留细胞之间的相对关系。这种低维嵌入是分解细胞异质性和重建细胞类型特异性基因调控程序所必需的。然而，传统的降维技术在计算效率和全面解决不同分子模式的细胞多样性方面面临挑战。

附：英文原文

Title: A fast, scalable and versatile tool for analysis of single-cell omics data

Author: Zhang, Kai, Zemke, Nathan R., Armand, Ethan J., Ren, Bing

Issue&Volume: 2024-01-08

Abstract: Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.

DOI: 10.1038/s41592-023-02139-9

Source: https://www.nature.com/articles/s41592-023-02139-9